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Molybdate Recovery by Adsorption onto Silica Matrix and Iron Oxide Based Composites.

Florin MatusoiuAdina NegreaMihaela CiopecNarcis DuţeanuPetru NegreaPaula SveraCatalin Ianăși
Published in: Gels (Basel, Switzerland) (2022)
Aggressive industrial development over the last century involved different heavy metals being used, including high quantities of molybdenum, which need to be treated before discharge in industrial waters. Molybdenum's market price and industrial applicability make its recovery a big challenge. In the present study the possibility to recover molybdenum ions from aqueous solutions by adsorption on a composite material based on silica matrix and iron oxides-SiO 2 FexOy-was evaluated. Tests were performed in order to determine the influence of adsorbent material dose, initial solution pH, contact time and temperature over adsorption capacity of synthesized adsorbent material. For better understanding of the adsorption process, the obtained experimental data were modelled using Langmuir, Freundlich and Sips adsorption isotherms. Based on the obtained data, it can proved that the Sips isotherm was describing with better orderliness the studied process, obtaining a maximum adsorption capacity of 10.95 mg MoO42- for each gram of material. By modelling the studied adsorption process, it was proven that the pseudo-second order model is accurately describing the adsorption process. By fitting experimental data with Weber-Morris model, it was proven that MoO42- adsorption is a complex process, occurring in two different steps, one controlled by diffusion and the second one controlled by mass transfer. Further, studies were performed in order to determine the optimum pH value needed to obtain maximum adsorption capacity, but also to determine which are the adsorbed species. From pH and desorption studies, it was proven that molybdate adsorption is a physical process. In order to establish the adsorption mechanism, the thermodynamic parameters (ΔG0, ΔH0 and ΔS0) were determined.
Keyphrases
  • aqueous solution
  • heavy metals
  • wastewater treatment
  • big data
  • physical activity
  • machine learning
  • artificial intelligence
  • drinking water
  • reduced graphene oxide
  • anaerobic digestion